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9 changed files with 16 additions and 98 deletions

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@ -1,48 +0,0 @@
name: "Build and draft a release"
on:
workflow_dispatch:
push:
tags:
- "v[0-9]+.[0-9]+.[0-9]+"
permissions:
contents: write
discussions: write
jobs:
build-and-draft-release:
runs-on: ubuntu-latest
steps:
- name: Checkout
uses: actions/checkout@v4
with:
fetch-depth: 0
- name: Set up Python environment
uses: actions/setup-python@v5
with:
python-version: "3.x"
- name: Build package
run: |
pip install build
python -m build
- name: Remove `v` in tag name
uses: mad9000/actions-find-and-replace-string@5
id: tagNameReplaced
with:
source: ${{ github.ref_name }}
find: "v"
replace: ""
- name: Draft a release
uses: softprops/action-gh-release@v2
with:
discussion_category_name: New releases
draft: true
generate_release_notes: true
files: |
dist/arcaea_offline_ocr-${{ steps.tagNameReplaced.outputs.value }}*.whl
dist/arcaea-offline-ocr-${{ steps.tagNameReplaced.outputs.value }}.tar.gz

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@ -4,7 +4,7 @@ build-backend = "setuptools.build_meta"
[project]
name = "arcaea-offline-ocr"
version = "0.0.99"
version = "0.0.97"
authors = [{ name = "283375", email = "log_283375@163.com" }]
description = "Extract your Arcaea play result from screenshot."
readme = "README.md"
@ -16,8 +16,8 @@ classifiers = [
]
[project.urls]
"Homepage" = "https://github.com/ArcaeaOffline/core-ocr"
"Bug Tracker" = "https://github.com/ArcaeaOffline/core-ocr/issues"
"Homepage" = "https://github.com/283375/arcaea-offline-ocr"
"Bug Tracker" = "https://github.com/283375/arcaea-offline-ocr/issues"
[tool.isort]
profile = "black"
@ -25,14 +25,3 @@ src_paths = ["src/arcaea_offline_ocr"]
[tool.pyright]
ignore = ["**/__debug*.*"]
[tool.pylint.main]
# extension-pkg-allow-list = ["cv2"]
generated-members = ["cv2.*"]
[tool.pylint.logging]
disable = [
"missing-module-docstring",
"missing-class-docstring",
"missing-function-docstring"
]

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@ -1,5 +1,5 @@
from datetime import datetime
from typing import Optional
from typing import Optional, Union
import attrs

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@ -67,9 +67,8 @@ class DeviceOcr:
roi = self.masker.score(self.extractor.score)
contours, _ = cv2.findContours(roi, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
for contour in contours:
if (
cv2.boundingRect(contour)[3] < roi.shape[0] * 0.6
): # h < score_component_h * 0.6
x, y, w, h = cv2.boundingRect(contour)
if h < roi.shape[0] * 0.6:
roi = cv2.fillPoly(roi, [contour], [0])
return ocr_digits_by_contour_knn(roi, self.knn_model)
@ -80,7 +79,6 @@ class DeviceOcr:
self.masker.rating_class_prs(roi),
self.masker.rating_class_ftr(roi),
self.masker.rating_class_byd(roi),
self.masker.rating_class_etr(roi),
]
return max(enumerate(results), key=lambda i: np.count_nonzero(i[1]))[0]

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@ -6,8 +6,6 @@ from .common import DeviceRoisMasker
class DeviceRoisMaskerAuto(DeviceRoisMasker):
# pylint: disable=abstract-method
@staticmethod
def mask_bgr_in_hsv(roi_bgr: Mat, hsv_lower: Mat, hsv_upper: Mat):
return cv2.inRange(
@ -34,9 +32,6 @@ class DeviceRoisMaskerAutoT1(DeviceRoisMaskerAuto):
BYD_HSV_MIN = np.array([170, 50, 50], np.uint8)
BYD_HSV_MAX = np.array([179, 210, 198], np.uint8)
ETR_HSV_MIN = np.array([130, 60, 80], np.uint8)
ETR_HSV_MAX = np.array([140, 145, 180], np.uint8)
TRACK_LOST_HSV_MIN = np.array([170, 75, 90], np.uint8)
TRACK_LOST_HSV_MAX = np.array([175, 170, 160], np.uint8)
@ -90,10 +85,6 @@ class DeviceRoisMaskerAutoT1(DeviceRoisMaskerAuto):
def rating_class_byd(cls, roi_bgr: Mat) -> Mat:
return cls.mask_bgr_in_hsv(roi_bgr, cls.BYD_HSV_MIN, cls.BYD_HSV_MAX)
@classmethod
def rating_class_etr(cls, roi_bgr: Mat) -> Mat:
return cls.mask_bgr_in_hsv(roi_bgr, cls.ETR_HSV_MIN, cls.ETR_HSV_MAX)
@classmethod
def max_recall(cls, roi_bgr: Mat) -> Mat:
return cls.gray(roi_bgr)
@ -125,7 +116,7 @@ class DeviceRoisMaskerAutoT1(DeviceRoisMaskerAuto):
class DeviceRoisMaskerAutoT2(DeviceRoisMaskerAuto):
PFL_HSV_MIN = np.array([0, 0, 248], np.uint8)
PFL_HSV_MAX = np.array([179, 40, 255], np.uint8)
PFL_HSV_MAX = np.array([179, 10, 255], np.uint8)
SCORE_HSV_MIN = np.array([0, 0, 180], np.uint8)
SCORE_HSV_MAX = np.array([179, 255, 255], np.uint8)
@ -142,9 +133,6 @@ class DeviceRoisMaskerAutoT2(DeviceRoisMaskerAuto):
BYD_HSV_MIN = np.array([170, 50, 50], np.uint8)
BYD_HSV_MAX = np.array([179, 210, 198], np.uint8)
ETR_HSV_MIN = np.array([130, 60, 80], np.uint8)
ETR_HSV_MAX = np.array([140, 145, 180], np.uint8)
MAX_RECALL_HSV_MIN = np.array([125, 0, 0], np.uint8)
MAX_RECALL_HSV_MAX = np.array([145, 100, 150], np.uint8)
@ -196,10 +184,6 @@ class DeviceRoisMaskerAutoT2(DeviceRoisMaskerAuto):
def rating_class_byd(cls, roi_bgr: Mat) -> Mat:
return cls.mask_bgr_in_hsv(roi_bgr, cls.BYD_HSV_MIN, cls.BYD_HSV_MAX)
@classmethod
def rating_class_etr(cls, roi_bgr: Mat) -> Mat:
return cls.mask_bgr_in_hsv(roi_bgr, cls.ETR_HSV_MIN, cls.ETR_HSV_MAX)
@classmethod
def max_recall(cls, roi_bgr: Mat) -> Mat:
return cls.mask_bgr_in_hsv(

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@ -34,10 +34,6 @@ class DeviceRoisMasker:
def rating_class_byd(cls, roi_bgr: Mat) -> Mat:
raise NotImplementedError()
@classmethod
def rating_class_etr(cls, roi_bgr: Mat) -> Mat:
raise NotImplementedError()
@classmethod
def max_recall(cls, roi_bgr: Mat) -> Mat:
raise NotImplementedError()

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@ -36,7 +36,7 @@ class FixRects:
if rect in consumed_rects:
continue
x, _, w, h = rect
x, y, w, h = rect
# grab those small rects
if not img_height * 0.1 <= h <= img_height * 0.6:
continue
@ -46,7 +46,7 @@ class FixRects:
for other_rect in rects:
if rect == other_rect:
continue
ox, _, ow, _ = other_rect
ox, oy, ow, oh = other_rect
if abs(x - ox) < tolerance and abs((x + w) - (ox + ow)) < tolerance:
group.append(other_rect)

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@ -12,8 +12,7 @@ def phash_opencv(img_gray, hash_size=8, highfreq_factor=4):
"""
Perceptual Hash computation.
Implementation follows
http://www.hackerfactor.com/blog/index.php?/archives/432-Looks-Like-It.html
Implementation follows http://www.hackerfactor.com/blog/index.php?/archives/432-Looks-Like-It.html
Adapted from `imagehash.phash`, pure opencv implementation
@ -70,14 +69,14 @@ class ImagePhashDatabase:
self.partner_icon_ids: List[str] = []
self.partner_icon_hashes = []
for _id, _hash in zip(self.ids, self.hashes):
id_splitted = _id.split("||")
for id, hash in zip(self.ids, self.hashes):
id_splitted = id.split("||")
if len(id_splitted) > 1 and id_splitted[0] == "partner_icon":
self.partner_icon_ids.append(id_splitted[1])
self.partner_icon_hashes.append(_hash)
self.partner_icon_hashes.append(hash)
else:
self.jacket_ids.append(_id)
self.jacket_hashes.append(_hash)
self.jacket_ids.append(id)
self.jacket_hashes.append(hash)
def calculate_phash(self, img_gray: Mat):
return phash_opencv(

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@ -42,5 +42,5 @@ def apply_factor(item: T, factor: float) -> T:
def apply_factor(item, factor: float):
if isinstance(item, (int, float)):
return item * factor
if isinstance(item, Iterable):
elif isinstance(item, Iterable):
return item.__class__([i * factor for i in item])